Open access database of industry 4.0 tasks for the development of AI-based classifier

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dc.contributor.authorMongelli, Francescaen
dc.contributor.authorMenolotto, Matteoen
dc.contributor.authorO'Flynn, Brendanen
dc.contributor.authorDemarchi, Daniloen
dc.contributor.editorLarcher, L.en
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2024-02-28T09:54:38Z
dc.date.available2024-02-28T09:54:38Z
dc.date.issued2023-01-17en
dc.description.abstractRobots and humans coworkers are sharing more and more portions of the smart manufacturing globally, meeting the need for high flexibility and rapid changes in the production layout. To be fully effective, however, such transition from classic robotics to the so-called collaborative robotics has to address several open problems, mostly related with safety and task optimization. Promising answers are coming from the motion capture technology, where wearable and optoelectronic sensing devices are deployed to gather human centric data to provide the robots with some form of awareness respect with the human activity and position. Tracking the hand of the operator, in particular, offers many advantages as we use our hands to explore and interact with the surroundings and to communicate. This has been highlighted by the several works focusing on gesture hand configuration recognition. This work present HANDMI4, a new open access database of hand motion tracking data, which includes a wide range of static hand grasp configurations and some classic dynamic industry tasks. Such database was generated using two of the most mature technologies for motion capture: IMU-based data glove and camera-based triangulation. To test the capability of such dataset to foster AI-based task classifier, a set of machine learning techniques were implemented and tested. In particular, KNN weighted reached 94,4% and 100% of task classification accuracy for the data glove and the camera system, respectively. With this open access database we aim to boost the research around task classification through motion capture technology to enable the next revolution in smart manufacturing.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMongelli, F., Menolotto, M., O'Flynn, B. and Demarchi, D. (2023) Open access database of industry 4.0 tasks for the development of AI-based classifier', 2023 Smart Systems Integration Conference and Exhibition (SSI), Brugge, Belgium, 28-30 March, pp. 1-5. https://doi.org/10.1109/SSI58917.2023.10387755en
dc.identifier.doi10.1109/SSI58917.2023.10387755en
dc.identifier.endpage5en
dc.identifier.isbn979-8-3503-2506-5en
dc.identifier.isbn979-8-3503-0231-8en
dc.identifier.issued2en
dc.identifier.journaltitleIEEE Electron Device Lettersen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/15592
dc.identifier.volume45en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartof2023 Smart Systems Integration Conference and Exhibition (SSI), Brugge, Belgium, 28-30 Marchen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.rights© 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en
dc.subjectIndustry 4.0en
dc.subjectIMUen
dc.subjectMotion captureen
dc.subjectDatabaseen
dc.subjectAIen
dc.titleOpen access database of industry 4.0 tasks for the development of AI-based classifieren
dc.typeConference itemen
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